Join us at the AWS Summit NYC 2025 as we explore the cutting-edge advancements in artificial intelligence and cloud technology. Discover the transformative potential of these innovations and how they set new benchmarks in the tech industry.
Ben Schreiner of AWS shares insights from the AWS Summit in New York City. This session, hosted by theCUBE, highlights the company's latest advancements, including the much-anticipated release of AgentCore and the practical applications of generative AI in today's dynamic business environment.
In this engaging discussion, Schreiner explores how AWS continues to push the boundaries of AI and cloud solutions. With their expertise, they provide a deep dive into key topics such as agentic efficiency, secure deployment practices, and the crucial role of customer feedback in driving innovation. TheCUBE's expert analysis adds further insights, making this a must-watch for industry professionals.
Schreiner emphasizes AWS's commitment to enhancing customer interactions and accelerating AI adoption. According to them, the speed of integrating AI into business processes is pivotal, along with understanding the significance of a robust data strategy for maximizing AI's potential. The session provides valuable takeaways for organizations aiming to leverage AI for competitive advantage.
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Ben Schreiner, AWS | AWS Summit NYC 2025
In this AWS Mid-Year Leadership Summit interview, Rajiv Chopra, VP of Amazon Just Walk Out, joins theCUBE’s John Furrier to unpack the evolution and impact of computer vision in retail. Chopra shares how AWS has transformed the breakthrough technology behind Amazon Go into a scalable, edge-powered solution for partners across stadiums, hospitals, universities and airports. With over 250 deployments outside of Amazon properties, Just Walk Out is redefining how consumers shop by enabling fast, frictionless experiences without checkout lines.
Chopra details key benefits for retailers, from revenue growth to shrink reduction, and illustrates use cases across venues like Lumen Field, UC San Diego and Hudson News. He breaks down the technological architecture behind the scenes, including deep learning models, custom edge compute devices and cloud integration, and explains how Just Walk Out balances accuracy, performance and customer experience. The conversation also highlights the broader trend of digital-physical convergence and visual reasoning as a frontier for applied AI.
Watch to learn how AWS is turning real-world environments into intelligent, automated spaces – and how Just Walk Out is leading the charge in reimagining retail through innovation.
play_circle_outlineUnlocking AI Potential: AWS Summit NYC Showcases AgentCore and Nova with Focus on Security and Governance in Enterprise Applications
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play_circle_outlineCustomer obsession drives AWS product development and agent deployment strategies.
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play_circle_outlineNavigating the Rapid Evolution of AI: Leadership and Strategies for Organizational Adaptation
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play_circle_outlineData quality and governance are crucial for successful AI implementation.
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play_circle_outlineFocus on compressing time and enhancing customer experiences through AI technologies.
Head of AI and Modern Data Strategy Business DevelopmentAWS
In this exclusive interview from AWS Summit New York City, Ben Schreiner, head of AI and Modern Data Strategy Business Development at AWS, joins theCUBE’s John Furrier to unpack Amazon’s latest advances in generative and agentic AI. Speaking on the heels of major announcements like AgentCore and Kiro, Schreiner explores how AWS is accelerating the transition from proof-of-concept to production at enterprise scale.
The conversation highlights how AWS is helping customers go beyond “vibe coding” to build robust, secure and governed AI agents. Schreiner...Read more
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What recent developments has AWS made regarding code and innovations related to AgentCore?add
What are the key components of the leadership principles regarding customer interaction and feedback?add
What are the key factors for companies to remain competitive in the AI era?add
What is the importance of data quality and governance in the context of AI implementation in organizations?add
What is the role of AI in improving business processes and customer interactions?add
>> Hello, welcome to theCUBE. I'm John Furrier, your host here in the Big Apple, New York City for AWS Summit. We are on the ground floor in the Expo hall getting all the action. It's Cloud AI Week in New York City. Of course, theCUBE has this New York Stock Exchange studio and Palo Alto connecting Silicon Valley and Wall Street. But here we're on the ground getting all the action. Amazon is introducing new models around Nova, obviously the agent wave is here. AgentCore is the big news. Ben Schreiner is here, AWS lead on AI spokesperson. What's your title?>> I am the head of AI and Modern Data Strategy Business Development. It's a mouthful.>> Okay, so welcome to theCUBE. Thanks for coming on. Love the jacket.>> Thank you. Congrats.>> Hey, I love the agent wave and I met with Deepak, Matt Garmin and the team all last month, we rolled out a halftime report. And the programming side of it, and obviously pre-event, Kiro was released. The agentic efficiency and performance, you're starting to see a lot of meat on the bone relative to what the value is. It's not just vibe coding. And you starting to see now the configuration side of agents where people are thinking, okay, I love agents. You have me at agents. Automating things, great. But is it work? Are they delegated properly? Is it trusted? This is the top conversation. This has been big for AWS to have all this code shipping this early, it's not even re:Invent.>> It isn't re:Invent. and it's very exciting here in the Big Apple, big news of course, with the AgentCore coming out and really demonstrating our innovation ahead of the problems you could foresee that agents could create if they weren't governed, if they weren't observed, if they didn't have a secure runtime. So you can really see the innovation coming out of our engineering teams to help enterprises not only develop these agents, but also make sure that they're deploying them in a secure, reliable way.>> I've been following Amazon's office for such a long time as a customer in the early days when they first came out, and obviously with theCUBE going back to 2012, 2013 when re:Invent was kicking around. What I noticed is obviously you guys work backwards from the customer, always strong coding and development environment. You've been leaning in internally. We've talked to a lot of people in the team. But the customers are driving a lot of the requirements on how they see AIs. It's not like Amazon's version of agents, hey, here, take it and use it. There's a lot of back and forth. Can you share the customer side of this? Because I know there's a lot of engagements and we've documented a bunch of them because they have unique use cases and AIs great for personalization as well. So how is that customer motion working in some of the product development and also for them deploying agents in these early days?>> You hit on a key component of our leadership principles, that customer obsession as well as our mechanism for working backwards. And that's at the root of how we approach all customers, they're all unique and there may be some themes and trends that transcend customer segments or industries. But we want to take each customer's challenge and what they're trying to do and work backwards from that. And we get a lot of incredible feedback, we have some amazing customers that are incredibly demanding and have high expectations, which we love because it informs the roadmap, it tells us what they're trying to do, and we do our very best to incorporate that feedback into the engineering process. Our GenAI Lab, which we announced a new refreshed investment of another $100 million focused on the agentic boom, you're getting a lot of real-time feedback on what people want to be able to do. We can't anticipate all the needs, so we're listening and we're trying to incorporate that into our approach as we go forward.>> Vibe coding was so last year, but now engineering is this year's focus and you're starting to see with Kiro and the news around Nova, the customization also the AgentCore, this element of vibe coding to get going and then the configurations and some of those hooks that are needed to catch stuff to production. I mean that's one of the big themes that Matt Garmin posted on LinkedIn prior to the event and you're starting to see in the announcements is, okay, great vibe coding gets it going, ideation.>> Awesome.>> The speed to production has been the big conversation for the past year, you have great POCs out there, but now it's a production game. Can you share some examples of how that's going and how this news relates to accelerating?>> Yeah, I was super excited about Kiro. I'm so glad it's not a secret anymore. The opportunity to have vibe coding and non-technical people describe what it is they need to do their job or do whatever they want for their customers, that visualization and the components there help the development team who understands security, reliability, the needs of an enterprise. And Kiro brings those two things together for the first time where you're seeing the requirements and the documentation and all the things that need to go into creating a production-ready solution. So many customers got stuck in POC land and it's unfortunate, but if you don't get into production, then you didn't really solve the problem that you had identified. And so while POCs are great learnings, and I think we're all in learning phase for the last couple of years, it really is time to see the value and we take that customer-obsessed, but production and scale is the goal and we drive towards that with our customers.>> Ben, talk about the speed game because this has come up. It's been a theme. I was out in June previewing with the execs, kind of a halftime report, that was the common theme from all the execs, speed, velocity, pace of play in AI. It's been magical on one hand, but also it forces the change side of it quickly. Talk about how the change management inside organizations, because not only is it obvious that AI and agents are going to be a value proposition for business.>> Absolutely.>> The question is, okay, how do you put that into process? The people see the vibe coding, they see prompt engineering, they start to see these tools getting easier and easier. How is that change happening? Is it matching the speed because the machines are getting faster, we're on a road->> I think the machines are running ahead of us right now, if I'm honest. The change is absolutely critical and it comes down to leadership. From the top down you're seeing leaders say, we are going to approach problems differently. And that allows us and others to take a proactive, how do you bring your people along the journey? How do you give them tools so they can experiment? How do you create a culture of iteration? Gone are the days where you had 18 month life cycles of product development and release, now you're 18 day or 18 second depending on what you're trying to do. So the speed is there and I think some companies struggle with adaptation and those who are really going to be competitive in the AI era are going to be the ones that adapt quickly and are always seeking those outside influences, taking that, understanding how to turn it into a competitive advantage, leveraging the tools to do so.>> So I'd love your title, AWS, head of AI and Modern Data Strategy Business Development. Okay, that's a mouthful but let's unpack that.>> It is.>> Head of AI, which means obviously AI is the driving of all the change->> All conversations.>> Modern data strategy is a topic we've been unpacking a lot on theCUBE the past couple of years. Most recently, that data layer, you're starting to see SageMaker, Bedrock play a big role in the Amazon piece of it in the stack. Obviously platform engineering with Kubernetes rock solid and all the stuff going on on the platform engineering side. But now you look at the AI stack, what is the best strategy and what are some of the things you've seen around companies who are, I won't say fumbling, they're just kind of resetting their system architecture for what their distributed computing's going to look like. They want the large-scale performance of the AI infrastructure, and that's causing kind of a rethinking of their data states, their databases, the role of the databases, and you've got vibe coding and you've got these tools coming in, it's going to accelerate coding. It's forcing them now to look at this. What is the playbook? What is the best practice that you could share around the kind of profile customers? And is it by industry or is it by size? Give us a little taste of what you're seeing develop.>> So the best playbook and the pattern that we've noticed is customers having some success in POC where they've had a small data set, then they go to scale and it breaks. It doesn't meet the expectations. And quite frankly, if we go back to our route where we're working backwards from, what's the problem? It should be what's the problem? What is the data I need to solve this problem? Then you get into models and frameworks and so forth. Far too many have gone, I have a model, let's see what I can do with it. And are kind of doing the process unfortunately in reverse and having mixed results as you've highlighted. So I would say many customers have struggled with their data for a very long time. It usually was down on their priority list because they had other priorities. Now that AI is at the top of their priority list, the data dependency is critical. We need executives to understand that the machines and the agents that you create are only as good as the data they have access to. And so if you want good answers from your agentic workflows or anything you do with AI, then you've got to make sure the data it has access to is strong. And that gets to governance, it gets to security, it gets to all the things that we've been professing for decades now, if I'm honest. But I think we're now getting the ear of the C-suite where they're starting to understand that it's a critical component to success.>> When you're in meetings in the day in the life of what you do internally at AWS and also with customers, what are some of the conversations that you have? Is it more of, will the parachute open kind of thing? Are they more mean... I mean, no one jumps out of a plane without a parachute training class. So you got to do the work on the front end. And we're starting to see that if you do this stuff on the front end data scales->> It makes a difference.... >> because you start to look at now agents as a multiplier effect. And this has come up a lot. In last week I was in Paris talking about the cost of tokens and to start to look at how the models are being architected. You don't want to waste tokens on basic services.>> Oh my gosh, you don't.>> You could use compute, you could use other things, tooling is getting better. So there's a lot going on around AI that's changed. Can you share your perspective on what's the state-of-the-art kind of landscape on tooling, relationships to the models and how customers are managing that cost per token piece of it? Because that's where the money is spent.>> So I'll say this, I think there's a misnomer or a myth out there that there'll be one model or even one agentic framework to rule them all. Quite frankly, that's an oversimplification. And if it helps you understand the first step, okay. But hopefully soon you'll get past that and realize that models are all trained different, they're all good at different things. The frameworks have different purposes. And so really our approach is founded in choice. It's founded in aligning the return on investment for the problem that you're solving. And so I foresee and many of our conversations is you want that user interface to be as clean and simple and easy to understand as possible. But then behind that, you want something that can actually route the query to the right model or to the right framework to solve the problem. So agents are not just simple question, answer. They're going to get more complex workflows, more tools at their disposal, and that capability is awe-inspiring and has many people just eager to figure out, how do I get started? And the GenAI Lab is a great place to start that journey.>> Yeah. I want to get your thoughts on something that I'm observing and get your reaction to. One is the hot air actually is coding. You're seeing that with what you guys are announcing, obviously. The other area is the business model transformation in the customer side. And you start to see the use cases around sales and marketing. What data do they have? So marketing is a great area that's been innovating. So you've got the business logic side, which kind of bleeds into kind of the data analytics space, which is mature. So you got the analytics market converging in with this new generative software development environment that used to be called AppDev, I mean, can almost rename it and call it AgentDev. So you have this massive tsunami of speed development and business model transformation. Who's driving what here? What's the driver? Because you have pre-existing departments that have been doing analytics, data analytics, data warehousing, data in the cloud with this new era coming in. Is it a collision course? Does it marry well? How are customers leaning into this? Do they come in from the analytics side? What's the change driver?>> It's a spectrum, John, I mean for sure. All customers are different and coming at at a different level of maturity. The ones that have those practices I think have a leg up because they've got their arms around their data. If anything, there's an explosion of data pulling sources that you didn't use before, it's getting easier and easier to access. And then you couple in the agentic capabilities and now you're able to combine things in new and different ways that allow for new value to be generally created. And so it's an exciting time. And I think the big difference now is there is an overarching sense of urgency at the top of the house. There is a need to figure this out. And that has all the best minds trying to solve the problem. And that's exciting because usually right there in the mix, having those conversations. And it really is about the art of the possible. I like to say this, we have a leadership principle you're very familiar with, which is think big. And I'd like to even say it's think bigger. I don't think people are thinking big enough about what's possible and the transformation that's going to happen in their industry.>> I also learned about the think big principle yesterday from Myra, is the ladder approach too.>> Sure.>> Bring a ladder to the big idea.>> Yes.>> Take us through that because I think one of the things you mentioned I want to just unpack a little bit is that, yeah, analytics gave me a dashboard, and I'm oversimplifying it, but the idea, okay, what's going on in the business. I think you hit a good point, which is we're seeing the same thing, which is what problem can we solve to make our business better, customer experience better or the product better. It's zeroing in on those things and then working back, right? That's the working backwards->> Exactly.>> Tied that together.>> You nailed it.>> But this seems to where the C-suite's like, okay, all the dogma fashion about what tools this... We want to solve this problem, can it do it? So the first question is it possible?>> And you need to start->> And then, if yes, what do we do?>> I would say it starts with defining that problem. AI is an amazing ability to compress time. Any of the examples that could give you it usually is some time savings involved, other savings as well. But time savings is the easiest. So all business executives should be looking at their processes, their interactions with their customers for where are the areas where there's a lot of time spent and can we compress it? If it's customer service and I'm putting you on hold, can I avoid putting you on hold so I have a better customer satisfaction, better answer, time to resolution. All of those examples really are what's the best problem. Then you get to, can I solve it? So do I have the data and the right model to solve that problem efficiently? We've got both BMW and F1 have focused their agentic journey on root cause analysis. So they have an outage of some kind, there's some problem->> It's a pain point.>> Oh my gosh. F1, I believe it was 15 days to resolve and they've taken it down to now a day and triage is in under an hour. So if you can triage faster because you have agents gathering the information, which used to be manual and now it's being brought together automatically, you can really cut a lot of time and get some real value.>> Ben, you just hit a great point there because I think one of the things that would come up in some of the other conversations with Amazonian customers is that the evaluation go, no go decision of is it possible, would be like a meeting, get everyone together, scope the problem. What Kiro, I saw the demo yesterday was interesting because you can see that actually gives an answer.>> Yes.>> It actually does an analysis->> An evaluation. Yeah.... >> is it possible? So it's already doing agentic work on your behalf in line. It's almost like a developer's CICD pipeline for business.>> It really is combining those two things. I mean, as long as I've been in technology, the business has been separate from IT and that gap has been a challenge for many organizations. Kiro has a great opportunity to be that collaboration platform between the two groups to speed up that interaction and get to production. That's where the problem gets solved is when you're scaled in production.>> Yeah, I think the technology transformation story we've been talking about over a decade, but you just hit it. It's not just technology, it's the business model transformation, the business side. How is that affecting your job? Take us through what you do on a daily basis. Are you out talking to customers? Are you working internally? What is your day-to-day job and how are you using this new... Are you riding the wave? What's your surfboard look like? What do you do? How do you do your job?>> So my team is incredible. I've got a group of eight that actually have a great opportunity. We have something called the Competitive Advantage Program where we bring in an executive team to inspire them. We bring them into one of our fulfillment centers and they walk the 4 million square feet and see data and AI at a scale that is pretty impressive. And then we get into a discussion after they see what we're doing, Where we have a modern data strategy, we have a layer of intelligence all making the people more safe, effective, and efficient so that you get the package to your house faster. And hopefully you had success on Prime Day most recently. I certainly did. But that experience, once we're done with that tour, we then go in and talk about that company's current competitive advantage. We talk about how might that change be threatened or enhanced with the AI era. And then we talk about what can we do together to make sure your company is prepared to be successful in the AI era.>> You're customer facing?>> Correct.>> And you then go into Amazon's arsenal of examples and bring that showcase to the customer->> Exactly right.... >> and then map that to them. Okay. It brings to my final question, which is, this has come up, this is kind of counterintuitive, but give me your thoughts. One is you go back, say 5, 10 years ago you guys were organized and most companies were organized by industry.>> Yes.>> Oh yeah. Oil and gas, media. Now we're seeing AI mostly as scoped to, and I see if you disagree or disagree, but it's more like profile of the customer profile, if you're SMB. So the size of the customer is more adequate for mapping to what a comparable is. It's not by industry per se, because AI is kind of agnostic unless it's like labeling or some sort of->> I think it's an intersection. Because again, the data fuels the AI and the data by industry is very unique. I mean, our health scribe, doctors talk a totally different language than I do and I don't understand it. So the industries have their own languages. Financial services, I know you do a lot of work there, they have their own language. And so the data, the labeling of the data that feeds AI, I do think is germane to the industry. But you're right, the right size customer, I think it's an intersection of those two things->> It's cool because the language models or the foundation models have vertical models.>> Many do. Bloomberg created their own model with us. So you have industry-specific. But then you have the generic, the foundational models are generic and they have data from all industries. So really, again, working backwards from the problem, let's figure out the right mix of solutions.>> All right, so what's on your to-do list for the second half of the year and give us a look back. We're at the halfway point, a little bit over the halfway point to re:Invent. Give us an update on what's happened in the first half of the year for you and your organization, and what's your goals for the second half?>> Yeah, I mean, it's customer engagement mean. We're trying to ride the wave, as you said, and have as many engaging, inspiring conversations so that customers realize that we're here for the long haul. We're going to continue to innovate, we'll continue to develop on their behalf. But we really want to establish that partnership so that we are working with them to achieve their goal of growing their company and satisfying their customers. So I look forward in H2 to even more customer interactions. I look forward to all the customer interactions at re:Invent. That's a great opportunity to meet with customers. And right now it's just helping customers develop that strategy that they need, that vision, and then start executing and scale as fast as possible.>> For the customers that you haven't met with yet, have a quick plug on how to engage with you. What's the best motion? Is it a meeting? Is it a briefing and then you guys get together? Is it face-to-face?>> Yeah. I mean the best way to engage my team particularly is reach out to your account teams. Your account teams can get ahold of us. We'll source the right resources. But there are no customers right now, in my opinion, that have maximized the value they could get out of AWS. So communicate what you're trying to do so that our teams can try and bring all of the resources we have to bear to help you achieve your goals.>> You got a lot of partners to the ecosystem.>> Oh, the partner ecosystem is vast and they're here. The expo is full of software providers that are built on top of AWS and the combination of ISVs, integration partners and then the native tools is really a winning strategy.>> I was talking to an ecosystem partner the other day and they were telling me that in customer meetings, they're actually coding as a demo.>> Like they're live.>> And they're like, oh, that would be great if you can . I just did, click this link. So you're starting to see that kind of velocity.>> Yeah, you are. And the eyes get open, right?>> Yeah, that's a game changer.>> Well, it reinforces that sense of speed and urgency when you're doing that live with a customer. We're doing that as well. It's a lot of fun.>> Ben, great to see you. Thanks for coming on.>> It's a privilege.>> Great content. Okay, we're here on the ground. I'm John Furrier, your host of theCUBE. We are at the AWS Summit here in New York City. Again, it's AI Cloud Week for theCUBE, and again, we're doing our best to bring that content to you. Thanks for watching.
